Convolutional Auto Encoders (CAE), sometimes also referred to as Stacked Convolutional Auto Encoders, are a type of deep autoencoder that uses regional information to reconstruct inputs that are two- or three- dimensional.
In a standard autoencoder, the layers in the encoder and decoder are fully connected.
A CAE maintains the same structure as the standard autoencoder but replaces the fully-connected layers with convolutional layers when downsampling and transpose convolutional layers when upsampling [1]}.
Similarly, as the standard autoencoder, the CAE also learns abstractions from the data even when the CAE is complete, \(i.e.\)  when the dimensionality of the latent space is as large as the input [2]}.
